Causal Influence Control for Persistent Memory in Language Model Systems

Abstract

Persistent-memory language models do more than recall the past. They decide which past experiences get to steer present behavior. Most memory systems rank records by relevance, recency, summaries, or tool state. But they do not ask the harder question: what will this memory make the model do next? We treat recall as an inference-time intervention. Each memory carries a causal influence signature: an estimate of how it may shift future model behavior when it enters the workspace through context, memory tools, attention, or model state. A controller admits a memory only when its expected effect, side effects, policy risk, and reliability fit the task. It then observes what happened, records lineage, and rolls back or quarantines memories whose effects diverge from prediction.